1. Probability

2. Properties of probabilities 0 ≤ p(A) ≤ 1  0 = never happens  1 = always happens  A priori definition p(A) = number of events classifiable as A total number of classifiable events  A posteriori definition p(A) = number of times A occurred total number of occurrences

3. So: p(A)= nA/N = number of events belonging to subset A vs. the total possible (which includes A). If 6 movies are playing at the theater and 5 are crappy but 1 is not so crappy what is the probability that I will be disappointed? 5/6 or p =.8333

4. Probability in perspective Analytic (classical) view –The common approach: if there are 5 bad movies and one good one I have an 83% chance in selecting a bad one. –Fisher Relative Frequency view –Refers to the long run of events: the probability is the limit of chance i.e. in a hypothetical infinite number of movie weekends I will select a bad movie about 83% of the time –Neyman-Pearson Subjective view –Probability is akin to a statement of belief and subjective e.g. I always seem to pick a good one. –Bayesian

5. Experiment -- a Process that produces outcomes –More than one possible outcome –Only one outcome per trial Trial -- one repetition of the process Event -- an outcome of an experiment –may be an elementary event, usually represented by an uppercase letter, e.g., A, E 1 Experiment

6. Generally we can calculate the probability of one of a set of equally likely events by counting the sample space Many problems in probability can be solved in this way probability very often makes use of combinatorics (permutation and combination – we’ll talk about this later)

7. S = {(x,y) | x } x is the family selected on the first draw y is the family selected on the second draw Concise description of large sample spaces Sample Space -- Set Notation for Random Sample of Two Families

8. The set of all elementary events for an experiment Methods for describing a sample space –Listing –Venn diagram Sample Space

9. Experiment: randomly select, without replacement, two families from the residents of Denton Each ordered pair in the sample space is an elementary event, for example -- (D,C) Family Children in Household Number of Automobiles ABCDABCD Yes No Yes 32123212 Listing of Sample Space (A,B), (A,C), (A,D), (B,A), (B,C), (B,D), (C,A), (C,B), (C,D), (D,A), (D,B), (D,C) Sample Space -- Listing Example

10. Venn diagrams helps us pictorially represent many of the algebraic rules of probability The union of two sets contains an instance of each element of the two sets. Venn Diagram Y X Venn Diagrams and the union of sets

11. The intersection of two sets contains only those elements common to the two sets. Venn Diagram Y X Intersection of Sets

12. Definitions Mutually exclusive events –both events cannot occur simultaneously. Can’t be a junior and senior –Complementary events Two mutually exclusive events that are all inclusive Independent events: –occurrence of one event has no effect on the probability of occurrence of the other

13. Events with no common outcomes Occurrence of one event precludes the occurrence of the other event Venn Diagram Y X Mutually Exclusive Events

14. Sample Space A Complementary Events All elementary events not in the event ‘A’ are in its complementary event.

15. Exhaustive sets –set includes all possible events –the sum of probabilities of all the events in the set = 1 Equal likelihood –roll a fair die each time the likelihood of 1-6 is the same whichever one we get, we could have just as easily have gotten another –Counter example- put the numbers 1-7 in a hat. What’s the probability of even vs. odd?

16. AND vs. OR How do we find the probability of one event or another occurring? How do we find the probability of one event and another occurring?

17. Addition p(A or B) = p(A) + p(B) –Probability of getting a grape or lemon skittle in a bag of 60 pieces where there are 15 strawberry, 13 grape, 12 orange, 8 lemon, 12 lime? –p(G) = 13/60p(L) = 8/60 –13/60 + 8/60 = 21/60 =.35 or a 35% chance we’ll get one of those two flavors when we open the bag and pick one out

18. Y X General Law of Addition (not necessarily mutually exclusive)

19. .11.19.30.56.14.70.67.331.00 Married (Yes/No) YesNoTotal Yes No Total Children (Yes/No) Example: Marriage and Children

20. S N.56. 67.70 General Law of Addition

21. .11.19.30.56.14.70.67.331.00 Married (Yes/No) YesNoTotal Yes No Total Children (Yes/No) Contingency Table

22. Multiplication If A & B are independent p(A and B) = p(A)p(B) p(A and B and C) = p(A)p(B)p(C) Probability of getting a grape and a lemon after two draws (with replacement) from the bag –p(Grape)*p(Lemon) = 13/60*8/60 = ~.0288

23. Conditional probabilities If events X and Y are not independent then: p(X|Y) = probability that X happens given that Y happens –The probability of X “conditional on” or “given” Y occurs –It’s our ‘and’ type of question from before so we are going to use multiplication, however we don’t have independent events so it will be a little different p(A and B) = p(A)*p(B|A)

24. Example: once we grab one skittle we aren’t going to put it back (sampling without replacement) so: – p(A and B and C) = p(A)*p(B|A)*p(C|A,B) –Probability of getting grape and lemon on successive turns = p(G)*p(L|G) –(13/60)(8/59) =.0293

25. A conditional probability is one where you are looking for the probability of some event with some sort of information in hand, e.g. the odds of having a boy given that you had a girl already. A joint event or probability would be the probability of a combination of events e.g. that you have a boy and a girl for children Aside: In this case the conditional would be higher b/c if we knew there was already a girl that means they’re of child-rearing age, probably interested in having kids etc. We have some additional info that would help us if we were just drawing out people randomly from some population. Conditional and Joint Probabilities

26. N S.56.70 Conditional Probability Venn Diagram *note before with our previous conditional probability we were dealing with mutually exclusive events i.e. can’t be grape and lemon at same time

27. Gender and Political Affiliation RDIOTotals M311510 F30227 Totals613717 RDIOTotals M17.65.9 29.458.8 F17.6011.8 41.2 Totals35.25.917.741.2100%

28. Let’s do a conditional probability: If I have a male, what is the probability of him being in the ‘Other’ category? Formally: p(A|B) = p(A and B)/p(B) = p(O|M) = p(O and M)/p(M) = = (.412*.714)/.588=.5

29. Easier way by looking at table- there are 10 males and of those 10 (i.e. given that we are dealing with males) how many are “Other”? p(O|M) = 5/10 or 50%.

30. Joint Probability Example What is the probability of obtaining a Female Independent from this sample? In this case we’re looking for the joint probability of someone who is Female and Independent out of all possible outcomes: 2/17 = 11.8

31. Practice Internet 10 hrs/wk+ Internet < 10 hrs/wk Total Under 25501565 Over 25252045 Total7535110 a. What is the probability a person is over 25? b. What is the probability that people under 25 spend at least 10 hours on the internet? c. What is the probability that someone who does not spend 10 hours on the internet each week is over 25? d. What is the probability of picking someone who spends less than 10 hours/wk on the internet and is under 25?

32. a. 45/110 =.41 b. 50/65 =.77 c. 20/35 =.57 d. 15/110 =.14